Identification of biomarkers forperiodontal disease using theimmunoproteomics approach
Jesinda P. Kerishnan1, Sani Mohammad2, Muhamad ShaifunizamAlias2, Alan Kang-Wai Mu3, Rathna Devi Vaithilingam4,Nor Adinar Baharuddin4, Syarida H. Safii4, Zainal Ariff AbdulRahman5, Yu Nieng Chen6 and Yeng Chen1,7
1 Department of Oral Science & Craniofacial, Faculty of Dentistry, University of Malaya,
Kuala Lumpur, Malaysia2 Faculty of Dentistry, University of Malaya, Kuala Lumpur, Malaysia3 Laboratory of Biotechnology, Institute of Win Men Biotech, Sungai Bakap, Pulau Pinang,
Malaysia4Department of Restorative Dentistry, Faculty of Dentistry, University of Malaya, Kuala Lumpur,
Malaysia5 Department of Oro-Maxillofacial Surgical & Medical Sciences, Faculty of Dentistry, University
of Malaya, Kuala Lumpur, Malaysia6 Chen Dental Specialist Clinic, Kuching, Sarawak, Malaysia7 Oral Cancer Research and Coordinating Centre, Faculty of Dentistry, University of Malaya,
Kuala Lumpur, Malaysia
ABSTRACTBackground: Periodontitis is one of the most common oral diseases associated with
the host’s immune response against periodontopathogenic infection. Failure to
accurately diagnose the stage of periodontitis has limited the ability to predict disease
status. Therefore, we aimed to look for reliable diagnostic markers for detection or
differentiation of early stage periodontitis using the immunoprotemic approach.
Method: In the present study, patient serum samples from four distinct stages of
periodontitis (i.e., mild chronic, moderate chronic, severe chronic, and aggressive)
and healthy controls were subjected to two-dimensional gel electrophoresis (2-DE),
followed by silver staining. Notably, we consistently identified 14 protein clusters in
the sera of patients and normal controls.
Results: Overall, we found that protein levels were comparable between patients
and controls, with the exception of the clusters corresponding to A1AT,HP, IGKC and
KNG1 (p < 0.05). In addition, the immunogenicity of these proteins was analysed via
immunoblotting, which revealed differential profiles for periodontal disease and
controls. For this reason, IgM obtained from severe chronic periodontitis (CP) sera
could be employed as a suitable autoantibody for the detection of periodontitis.
Discussion: Taken together, the present study suggests that differentially expressed
host immune response proteins could be used as potential biomarkers for screening
periodontitis. Future studies exploring the diagnostic potential of such factors are
warranted.
Subjects Molecular Biology, Dentistry
Keywords Immunoblotting, Biomarker, Proteomics, Periodontal disease, Serum immunogenic
protein, Two-dimensional electrophoresis
How to cite this article Kerishnan et al. (2016), Identification of biomarkers for periodontal disease using the immunoproteomics
approach. PeerJ 4:e2327; DOI 10.7717/peerj.2327
Submitted 27 April 2016Accepted 14 July 2016Published 24 August 2016
Corresponding authorYeng Chen, [email protected]
Academic editorPraveen Arany
Additional Information andDeclarations can be found onpage 15
DOI 10.7717/peerj.2327
Copyright2016 Kerishnan et al.
Distributed underCreative Commons CC-BY 4.0
INTRODUCTIONPeriodontitis, a chronic inflammatory condition in periodontal supporting structures, is
the most common oral inflammatory disease and presents a dental biofilm as its primary
etiology (Loe, Theilade & Jensen, 1965). A harmonious balance between the microbiota
present in the dental biofilm and host immunity is a sign of periodontal health. However,
when this relationship is disturbed by an over stimulated or diminished immune response
toward periodontopathogens, periodontitis develops and may ultimately lead to tooth
loss (Bartold & Van Dyke, 2013).
There are two major forms of periodontitis, chronic periodontitis (CP) and aggressive
periodontitis (AP) (Armitage, 1999). CP is a slowly progressing disease, which can be
categorized as mild/early, moderate or severe based on clinical criteria, such as probing
pocket depth (PPD) and clinical attachment loss (CAL) (Armitage, 1999; Eke et al.,
2012; Wiebe & Putnins, 2000). CP has a high prevalence, with approximately 40% of
patients suffering from moderate forms and 8% diagnosed with severe forms (Albandar,
Brunelle & Kingman, 1999; Eke et al., 2012). In contrast, AP differs from CP based on
the rapid rate of disease progression in an otherwise clinically healthy patient, absence of
large accumulations of plaque/calculus, and familial aggregation of diseased individuals
(Armitage, 1999). AP is known to occur in 1–3% of the population (Susin, Haas &
Albandar, 2014).
Traditional diagnostic procedures (e.g., visual examination, tactile sensation, PPD,
CAL, plaque index, bleeding on probing, and radiographic assessment of alveolar
bone loss) are still currently practised and considered to be fundamental for diagnosis
(Hatem, 2012). That being said, the standard protocol for measuring CAL and PPD using
a manual probe was first described more than five decades ago, and very little has changed
since 1959 (Kinney, Ramseier & Giannobile, 2007). Although, such procedures might
measure accumulated previous disease at a specific site (Ramfjord, 1959), they lack
accuracy and are unable to predict ongoing disease activity (Chapple, 1997). For this
reason, there remains a fundamental need to develop accurate, precise, and reproducible
biomarkers to diagnose periodontal disease, similar to those used in medical disciplines
(Preshaw, 2009).
Immunoglobulin M (IgM) is a natural antibody that can bind to specific antigens
to which the host has never been exposed (Avrameas, 1991; Casali & Schettino,
1996; Coutinho, Kazatchkine & Avrameas, 1995; Pereira et al., 1986). This natural
immunoglobulin exhibits traits that permit it to bind to antigens as they invade, resulting
in complement activation as a first line defence mechanism. Moreover, IgM displays
multi-valency and naturally high avidities (Coutinho, Kazatchkine & Avrameas, 1995;
Fearon & Locksley, 1996; Tlaskalova-Hogenova et al., 1991). Therefore, low-level antigenic
or immunogenic proteins are known to drive IgM-induced immune responses (Lin
et al., 2007). In this regard, the participation of IgM in the early recognition of
bacteria in periodontal disease has been demonstrated (Vollmers & Brandlein, 2006).
Immunoproteomics is the large-scale study of proteins (proteomics) involved in the
immune response. A wide variety of potential periodontal proteomic markers are
Kerishnan et al. (2016), PeerJ, DOI 10.7717/peerj.2327 2/19
included within the immunoproteome, from immunoglobulins to bone remodelling
proteins. For this reason, the use of immunoproteomic approaches in combination with
current clinical examination tools could lead to the development of more reliable and
effective diagnostic methods to differentiate AP and CP patients. Therefore, this study
aims to use two-dimensional gel electrophoresis (2-DE) and immunoblotting approaches
to identify immunogenic proteins with diagnostic potential in sera from patients with
periodontitis.
MATERIALS AND METHODSClinical samplesSerum samples were obtained from the Malaysian Periodontal Database and Biobank
System (MPDBS) (Vaithilingam et al., 2015) (University of Malaya, Kuala Lumpur,
Malaysia) with approval from the Ethics Committee of the Faculty of Dentistry,
University of Malaya (DF PE1103/0037(L)). The study was conducted in accordance with
International Conference on Harmonisation Good Clinical Practice (ICH-GCP)
guidelines and the Declaration of Helsinki. Subjects were systemically healthy individuals
who had at least 12 teeth present (excluding third molars). Subjects, who had received
periodontal treatment or antibiotics within the past four months or were pregnant at
the time of recruitment were excluded from the study. All examinations on the subjects
were conducted by a qualified examiner. Written informed consent was obtained from
all subjects. In this study, a total of 90 serum samples were collected, which included
42 healthy controls and 48 patient specimens (collected from 9 mild CP, 12 moderate
CP, 19 severe CP (Eke et al., 2012), and 8 AP (Van Der Velden, 2005) patients).
Samples collection and preparationCollected samples were divided, classified, and labelled based on the representative stages
of periodontitis (i.e., healthy, mild CP, moderate CP, severe CP, and AP). The stages of
periodontitis were classified through clinical diagnostic criteria such as PPD and CAL,
which was based on the suggested CDC-AAP (Center for Disease Control & Prevention–
American Academy of Periodontology) case definitions (Eke et al., 2012). The samples
were pooled together by form of periodontitis (10 ml each) into separate microcentrifuge
tubes for later use as primary antibodies in the immunoproteomic assay. All samples
were stored at a constant temperature of -20 �C for further analysis through 2-DE.
Two-dimensional electrophoresis (2-DE)Two-dimensional electrophoresis (2-DE) was performed as previously described (Chen
et al., 2014; Chen et al., 2008). Unfractunated whole serum samples (10 ml) were lysed,
rehydrated in lysis buffer (2 M thiourea, 8 M urea, 4% CHAPS, 1% dithreitol and
2% pharmalyte), and subjected to isoelectric focusing in 11-cm rehydrated precast
Immobiline Drystrips pH 4–7 (GE Healthcare Bioscience, Uppsala, Sweden) overnight,
using the Protean Isoelectric Focusing Cell (Biorad Inc., Berkeley, CA, USA). The second-
dimensional separation of focused samples in the gel strips was performed through
sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) using 10% linear
Kerishnan et al. (2016), PeerJ, DOI 10.7717/peerj.2327 3/19
polyacrylamide gels. A previously described silver staining method was used to
develop the 2-DE gels (Heukeshoven & Dernick, 1988), and a modified silver staining
technique was used for mass spectrometry (MS) analysis (Snevechenko et al., 1996).
All samples were analysed in duplicate.
Image and statistical analysisThe ImageQuantTM LAS500 (GE Healthcare Bioscience, Uppsala, Sweden) was
used to visualize and store the silver-stained 2-DE gel images. Detection, matching,
and quantification of the distinct protein spots were performed using the Image
Master 2D Platinum Software, version 7.0 (GE Healthcare Bioscience, Uppsala, Sweden).
Normalization was subsequently performed to evaluate proteins that were differentially
expressed in the serum by correcting the spot quantification values and gel-to-gel
variation unrelated to expression changes. This was achieved by taking into account
total densities from the gel images (i.e., raw quantity of each gel spot was divided by
the total quantity of all spots within the gel). The percentage of a given protein taken
against total spot volume of all proteins (including the unresolved peptides) in the gel
was calculated to give a percent of volume contribution (vol%) (Chen et al., 2008). All
protein concentration values are presented as means of percentage volume (% volume) ±
standard deviations (SD). Statistical analysis was done using the Student’s t-test to analyse
differences between controls and patients. A p-value of less than 0.05 (p < 0.05) was
considered to be statistically significant.
Mass spectrometry analysis and database searchPrior to MS analysis, spots of interest were excised and subjected to in-gel tryptic
digestion using the commercially available Proteo ExtractTM. All-in-One Trypsin
Digestion Kit (Calbiochem, Darmstadt, Germany). MS analysis was then performed at
the Faculty of Biological Science Proteomic Centre, National University of Singapore.
The digested peptides were first mixed with 1.2 ml of CHCA matrix solution (5 mg/ml of
cyano-4-hydroxy-cinnamic acid in 0.1% trifluoroacetic acid (TFA) and 50% acetonitrile
(ACN)) and spotted onto MALDI target plates. An ABI 4800 Proteomics Analyzer
MALDI-TOF/TOF Mass Spectrometer was used for spectra analysis (Applied Biosystems,
CA, USA). The MASCOT search engine (version 2.1; Matrix Science, London, UK) was
employed for database searches after MS analysis. In addition, GPS Explorer software
(version 3.6; Applied Biosystems, CA, USA) was utilized along with MASCOT to identify
peptides and proteins. The applied search parameters allowed for N-terminal acetylation,
C-terminal carbamidomethylation of cysteine (fixed modification), and methionine
oxidation (variable modification). Furthermore, the peptide and fragment mass
tolerance settings were 100 ppm and 60.2 Da, respectively. In addition, the peptide
mass fingerprinting (PMF) parameters were set as follows: one missed cleavage allowed
in the trypsin digest, monoisotropic mass value, 60.1-Da peptide mass tolerance, and
1+ peptide charge state. During the initial phase, peptides were identified using the
ProteinPilot proteomics software on the Mass Spectrometer (Applied Biosystems,
Foster City, CA, USA). A score reflecting the relationship between theoretically and
Kerishnan et al. (2016), PeerJ, DOI 10.7717/peerj.2327 4/19
experimentally determined masses was calculated and assigned. These analyses were
conducted using the International Protein Index (http://www.ebi.ac.uk/IPI) and NCBI
Unigene human databases (version 3.38). A score of > 82 was considered to be significant
in the MASCOT NCBI database.
ImmunoblottingThe 2-DE analysed sera were grouped into 25 categories based on immunoblotting
with primary antibodies (pooled sera) from healthy controls and the four stages of
periodontitis (Table 1). The Trans-blot� TurboTM Transfer Starter System (Biorad Inc.,
Berkeley, CA, USA) was used to transfer the separated proteins from the 2-DE gels
onto nitrocellulose membranes (0.45 mm) (Biorad Inc., Berkeley, CA, USA). Blotted
nitrocellulose membranes were then blocked using Superblock (Pierce, Rockford, IL,
USA) for 1 h and subsequently incubated overnight (4 �C) with IgM (pooled sera
from patients or controls that contain the primary antibodies against various targets)
as shown in Table 1. After incubation, membranes were washed three times (5 min each)
Table 1 Incubation of the 2-DE blotted nitrocellulose membranes with sera of patients or controls
(as primary antibody) was conducted overnight (4�) prior to monoclonal anti-human IgM-HRP.
Category Patient sera Probed primary antibody (pooled sera)
1 Normal Normal
2 Normal Mild chronic periodontitis
3 Normal Moderate chronic periodontitis
4 Normal Severe chronic periodontitis
5 Normal Aggressive periodontitis
6 Mild chronic periodontitis Normal
7 Mild chronic periodontitis Mild chronic periodontitis
8 Mild chronic periodontitis Moderate chronic periodontitis
9 Mild chronic periodontitis Severe chronic periodontitis
10 Mild chronic periodontitis Aggressive periodontitis
11 Moderate chronic periodontitis Normal
12 Moderate chronic periodontitis Mild chronic periodontitis
13 Moderate chronic periodontitis Moderate chronic periodontitis
14 Moderate chronic periodontitis Severe chronic periodontitis
15 Moderate chronic periodontitis Aggressive periodontitis
16 Severe chronic periodontitis Normal
17 Severe chronic periodontitis Mild chronic periodontitis
18 Severe chronic periodontitis Moderate chronic periodontitis
19 Severe chronic periodontitis Severe chronic periodontitis
20 Severe chronic periodontitis Aggressive periodontitis
21 Aggressive periodontitis Normal
22 Aggressive periodontitis Mild chronic periodontitis
23 Aggressive periodontitis Moderate chronic periodontitis
24 Aggressive periodontitis Severe chronic periodontitis
25 Aggressive periodontitis Aggressive periodontitis
Kerishnan et al. (2016), PeerJ, DOI 10.7717/peerj.2327 5/19
with TBS-T (Tris-buffered saline Tween-20, pH 7.5). The membranes were then incubated
with monoclonal anti-human IgM conjugated to horseradish peroxidase (HRP)
(Invitrogen, Carlsbad, CA, USA) at a dilution of 1:6,000 for 1 h at room temperature.
Finally, the membranes were developed with chemmiluminescent (ECL) substrate
(Pierce Rockford, IL, USA) and visualized with ImageQuant LA500 (GE Healthcare
Bioscience, Uppsala, Sweden).
Functional annotation enrichment and protein interaction analysisFunctional annotation enrichment and protein interactions were performed using
web-based bioinformatics tools. DAVID v6.7 (Database for Annotation, Visualization
and Integrated Discovery; http://david.abcc.ncifcrf.gov) was used for protein functional
enrichment analysis. DAVID v6.7 is a web-based integrated biological knowledgebase
and analytical tool that systematically extracts biological meaning from large lists of genes
and proteins (Huang, Sherman & Lempicki, 2008). This functional annotation was
considered to be significant when a p-value of less than 0.05 (p < 0.05) was obtained.
Furthermore, the identified proteins were evaluated using STITCH v4.0 (Search Tool
for the Retrieval of Interacting Genes; http://david.abcc.ncifcrf.gov), which is a web-based
resource to explore known and predicted interactions between proteins and chemicals
(Kuhn et al., 2008). Furthermore, the proteins found to be differently expressed in this
study were subjected to Ingenuity Pathway Analysis (IPA), which was used to identify
their relationships with other protein networks within the knowledge database through
computational algorithms. The highest-scoring networks were analysed to extract
relationships between pathways that might link candidate proteins to one another.
RESULTS AND DISCUSSIONImage analysis of 2-DE serum protein profilingHigh-resolution serum proteome profiles from normal controls and periodontitis
patients (i.e., mild CP, moderate CP, severe CP, and AP) were obtained through 2-DE
separation and silver staining of unfractionated serum samples. Fourteen major protein
clusters were consistently resolved in all 2-DE silver-stained profiles. Representative 2-DE
serum protein profiles from all categories of periodontitis patients and controls are
presented in Fig. 1.
Identification of expressed biomarkers using mass spectrometryThe identity of the 2-DE detected proteins was further verified by subjecting the
protein spot clusters to MALDI-TOF/TOF analysis, followed by database searches.
The protein clusters were identified as albumin (ALB), a2-HS glycoprotein (AHSG),
a1-antitrypsin (A1AT), a1-B glycoprotein (A1BG), Complement C3 (C3), haptoglobin
(HP), hemopexin (HPX), Ig alpha-1 chain C region (IGHA1), Ig gamma-3 chain C region
(IGHG3), Ig kappa chain C region (IGKC), Ig mu chain C region (IGHM/MU),
kininogen (KNG1), transferrin (TRF), and vitamin D-binding protein (VTD). All of
the proteins were aberrantly expressed in all 4 categories of periodontitis as compared
to normal control. Data regarding protein entry name, protein name, gene name,
Kerishnan et al. (2016), PeerJ, DOI 10.7717/peerj.2327 6/19
accession number, nominal mass and MOWSE protein score for each protein are
presented in Table 2.
We further analysed the average percentage of volume contributions of the 14 serum
proteins detected in control and periodontitis samples. Overall, we found that protein
levels were comparable in periodontitis patients and controls, with the exception of A1AT,
HP, IGKC, and KNG1 (Fig. 2). Notably, KNG1 expression was significantly lower in severe
A) B)
C) D)
E)
Figure 1 (A) 2-DE serum protein profiles of normal sera, (B) mild/early chronic periodontitis sera,
(C) moderate chronic periodontitis sera, (D) severe chronic periodontitis sera, (E) and aggressive
periodontitis sera. All serum samples of controls and patients were subjected to 2-DE and silver
staining. Protein spot were evaluated and analysed using Image Master Platinum version 7.0. The acidic
side of the membranes is to the left and the relative molecular mass declines from the top.
Kerishnan et al. (2016), PeerJ, DOI 10.7717/peerj.2327 7/19
CP and AP patients. On the other hand, A1ATwas decreased by two-fold in AP patients,
and HP was decreased in moderate CP, severe CP and AP. In fact, the fold changes
(down regulation) of these factors in distinct stages of periodontitis were as follows: 0.42,
0.53, 0.52, 0.79, 0.68 and 0.67, respectively (p < 0.05). Interestingly, the level of HP
was increased in mild CP patients, whereas IGKC was the only protein significantly
increased in severe CP patients (fold up-regulation: 1.46 and 1.28, respectively; p < 0.05).
Thus, these aberrantly expressed proteins might represent effective candidate biomarkers
for use in the development of future diagnostic tools for periodontitis disease.
Differently expressed proteins in periodontits patientsThe comparative analysis performed in this study revealed down regulation of A1AT,
HP, and KNG1 in different categories of periodontitis. The expression of these three
proteins was significantly lower in AP, while only HP and KNG1 showed low expression in
severe CP. On the other hand, HP was the only protein found to be significantly
diminished in moderate CP.
KNG1 belongs to a group of thiol protease inhibitors known as kininogens, which act
as inflammatory mediators and cause an increase in vascular permeability to release
prostaglandin, another important regulator of inflammation (Iarovaia, 2000). Our data
suggest that sera from patients with severe CP and AP physiologically lacked KNG1.
This loss of KNG1 from patient sera might be due to the association of kinins (released
from kininogens) with tissue kallikrein. In this regard, the release of kinins from
kinninogens is triggered by neutrophil elastase and could result from immune recognition
of periodontopathogens (Imamura, Potempa & Travis, 2004).
HP is encoded by the HP gene in humans (Dobryszycka, 1997; Wassell, 1999). It is
commonly considered as an acute-phase reactant and participates in haemoglobin
Table 2 Mass spectrometric identification of host-specific protein spot clusters from serum protein
profiles using the MASCOT search engine and the SWISS-PROT database accessed on 30.11.2014.
Protein entry
name
Protein name Gene name Accession
number
Nominal
mass (kDa)
MOWSE
protein score
AHSG a-2-HS glycoprotein AHSG P02765 39 63
ALB Serum albumin ALB P02768 69 386
A1AT a-1-antitrypsin SERPINA 1 P01009 46 68
A1BG a-1B glycoprotein A1BG P04217 54 107
C3 Complement C3 C3 P01024 187 65
HP Haptoglobin HP P00738 45 98
HPX Hemopexin HPX P02790 51 95
IGHA1 Ig alpha-1 chain C region IGHA1 P01876 37 61
IGHG3 Ig gamma-3 chain C region IGHG3 P01860 41 62
IGKC Ig kappa chain C region IGKC P01834 11 69
IGHM/MU Ig mu chain C region IGHM P01871 49 326
KNG1 Kininogen 1 KNG1 P01042 71 66
TF Serotransferrin TF P02787 77 61
VTDB Vitamin D-binding protein GC P02774 52 71
Kerishnan et al. (2016), PeerJ, DOI 10.7717/peerj.2327 8/19
binding in blood (Ebersole et al., 1997; Kapralov et al., 2009). In addition, HP plays a role
in host defence towards pathogens (Eaton, Brandt & Lee, 1982). Our analysis of patient
sera revealed that HP expression distinctly correlated with the stage of periodontitis.
Indeed, HP levels were higher in mild CP patients and gradually decreased with disease
progression. The high levels of HP in mild CP may result from initial bacterial
pathogenicity in periodontal tissues. In contrast, decreased HP expression in advanced
stages of periodontitis could be caused by formation of haemoglobin–haptoglobin
complexes that make haemoglobin iron unavailable to bacterial iron-binding proteins
(Eaton, Brandt & Lee, 1982). In addition, the correlative expression of HP may reflect the
acute phase response of the body (Chen, Lim & Hashim, 2009). Taken together, these
findings highlight the potential prognostic value of HP for monitoring the progress of CP.
A1AT is a serine protease inhibitor (Saunders et al., 2012) that contributes to host
defence against invading pathogens by inhibiting their proteolytic enzymes (Carrell, 1986;
0
1
2
3
4
5
6
7
8
9
10
N E M S A
Mea
n %
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e co
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on
Stages of periodon��s
HP
*
* *
*
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
N E M S A
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n %
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Stages of periodon��s
KNG
* *
0
1
2
3
4
5
N E M S A
Mea
n %
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olum
e co
ntri
bu�
on
Stages of periodon��s
AAT
*
0
5
10
15
20
25
30
35
N E M S A
Mea
n %
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olum
e co
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bu�
on
Stages of periodon��s
IGKC
*
Figure 2 Percentage of volume contribution (vol%) of significantly detected protein clusters in controls (N), and periodontis patient (mild/
early chronic periodontitis (E), moderate chronic periodontitis (M), severe chronic periodontitis (S), and aggressive periodontitis (A)).
Asterisks (�) indicates significantly differentiated expression (p < 0.05).
Kerishnan et al. (2016), PeerJ, DOI 10.7717/peerj.2327 9/19
Starkey & Barrett, 1977). Our data reveal that A1AT levels are decreased by two fold in AP
patients, which could be due to the capacity of the periodontopathogens to produce
proteolytic enzymes that deplete A1AT. Interestingly, our finding is consistent with a
previous report on Porphyromonas gingivalis, which is present in deep periodontal pockets
of periodontitis patients (Carlsson et al., 1984).
IGKC functions as an antigen-binding agent in humans, and therefore selectively
interacts with immunogens. IGKC binding can subsequently alter the biological activity of
an antigen. In addition, IGKC is involved in complement activation for direct killing
of microbes, immune regulation, and disposal of immune complexes (Gottlieb et al.,
1970). This might provide a functional explanation for the higher amounts of IGKC
seen in severe CP and AP patients.
Immunogenic protein identification through 2-DE immunoblottingIn order to further investigate the immunoproteomics analyses, 2-DE immunobloting
using periodontitis and normal control sera was performed according to the 25 conditions
listed in Table 1. The use of normal sera against normal and periodontitis sera was to
verify that reactions were restricted to periodontitis. Notably, only IGHG3 was detected
in the normal control.
IgM antibodies are secreted by B cells upon stimulation with primary antigen and
are important for initial defence mechanisms (Boes et al., 1998). In this regard, IgM is
capable of recognizing several pathogens, including lymphocytic choriomeningitis virus
(LCMV), Listeria monocytogenes, vaccinia virus (vacc-WR), and vesicular stomatitis virus
(VSV) (Boes, 2000). Therefore, the identification of immunogenic proteins recognized
by IgM in patient sera could allow for the early detection of periodontitis with high
specificity and sensitivity.
The use of 2-DE immunoblots and MS identification confirmed that all of the
observed proteins were host-specific. In addition, it revealed that the immunoblot
profiles were distinctive for periodontitis patients and controls, with the exception of
categories 6, 16, 17, 18, 20, 22, and 23 (Table 1). The proteins in these categories were
not reactive to IgM in the natural condition. Figure 3 illustrates the presence of
immunogenic proteins within each category. Based on this immunobloting technique,
we found that HP, IGKC, and IGHM of moderate CP were immunoreactive towards
the primary antibody (IgM). On the other hand, ALB, HPX, IGHM, and IGKC
could be detected in category 19, which corresponded to severe CP serum probed
with sever CP IgM.
Furthermore, our immunoproteomic data demonstrated that IgM from severe CP
patients was the sole autoantibody capable of detecting host specific immunogenic
proteins from all groups of periodontitis and controls (Table 3). Four host proteins
(ALB, IGHM, IGKC, and TRF) specifically interacted with the IgM from severe CP
and severe CP patient serum. Notably, these factors are known to be involved in
bacterial invasion processes (Guevara et al., 2012; Jefferis & Lefranc, 2009; Matsuzaki
et al., 1999; Skaar, 2010), further supporting their presence in periodontitis. Also,
IgM of severe CP was capable of differentiating periodontitis stages as well as normal
Kerishnan et al. (2016), PeerJ, DOI 10.7717/peerj.2327 10/19
controls. This indicates that these proteins could be used as potential biomarkers
to diagnose and differentiate the stages of periodontitis, improving the accuracy of
current methods.
Based on the immunoblot profiling analysis, we also observed a unique feature of
three host-specific proteins from moderate CP, namely HP, IGHM and IGKC. These
Figure 3 Immunogenic proteins present on the 2-DE immunoblotted nitrocellulose membrane. All serum samples of patients and controls were
subjected to 2-DE and blotted onto the nitrocellulose membrane prior to probing with pooled sera and monoclonal anti-human IgM-HRP.
Kerishnan et al. (2016), PeerJ, DOI 10.7717/peerj.2327 11/19
proteins presented high immunoreactivity to the IgM, suggesting that they are common
proteins recognized by the autoantibody.
Functional annotation and protein interaction analysisFunctional annotation analysis was performed for our 14 candidate host-specific proteins
using DAVID v6.7 and IPA. The associated gene ontology (GO) terms (i.e., biological
process, cellular component and molecular function) were annoted based on the database.
Additionally, in order to further investigate the signalling pathways of the identified
proteins, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to
perform pathway annotation and enrichment analysis. Indeed, significant categories
could be identified through the expression analysis systemic explorer (EASE) score,
which is a modified Fisher Exact p-value (threshold of significance set at p < 0.05).
IPA analysis identified three significant networks with scores of 18, 3 and 3, respectively
(Table 4). The highest-scoring network, which comprises 14 of the identified proteins
in our study, included the following functional processes: inflammatory response,
cellular growth, proliferation, and cellular function/maintenance. The remaining two
networks showing significant scores involved processes such as cell death and survival,
cellular assembly/organization and disease (i.e., cancer, gastrointestinal disease, and
hepatic disease).
Analysis of GO biological processes revealed that A1AT, AHSG, C3, KNG1, and
TF are involved in host defence, inflammatory response, acute inflammation, and
wound responses. Based on the KEGG pathway analysis, we found that A1AT, KNG1
and C3 were also associated with the complement and coagulation cascades. The
complement system is a known mediator of innate immunity and constitutes a non-
specific defence mechanism against pathogens (Markiewski et al., 2007). Furthermore,
IPA analysis revealed that nine proteins identified in our study are associated with
the inflammatory response, cellular growth/proliferation, and cellular function/
maintenance (Table 4). All of these annotations are consistent with the immediate
response of the host to infection or injury in periodontal disease. Therefore, since it is
known that periodontal disease is mediated by host defence and the inflammation (Cekici
et al., 2014), these findings emphasize the role of these identified proteins in periodontitis.
In addition, STITCH v4.0 was employed to identify binding partners for the
proteins identified in this study and to generate a protein interaction network
(Fig. 4). STITCH v4.0 is a web-based resource to explore known and predicted
Table 3 Incubation of the 2-DE blotted nitrocellulose membranes with pool sera of SCP patients as
primary antibody was conducted overnight at 4� prior to monoclonal anti-human IgM-HRP.
Patients sera Primary antibody Immunogenic proteins detected
Normal SCP pooled sera ABG, ALB, IGHM
ECP SCP pooled sera ALB, TRF
MCP SCP pooled sera HP, KNG, ALB, TRF, IGHG3, IGKC
SCP SCP pooled sera ALB, TRF, IGHM, IGKC
AP SCP pooled sera ALB, TRF, IGHG3, IGKC
Kerishnan et al. (2016), PeerJ, DOI 10.7717/peerj.2327 12/19
Table 4 Functional annotation analysis of 14 identified proteins using the DAVID bioinformatics resource (DAVID v6.7). (A) Gene Ontology
term. Top highest relevant enriched gene ontology terms with EASE score of p < 0.05 (a modified Fisher Exact p-Value) are listed for “Biological
Process,” “Cellular Component,” and “Molecular Function,” respectively. (B) KEGG Pathway analysis by DAVID bioinformatics resource. (C)
Functional enrichment analysis of 14 identified proteins using Ingenuity Pathway Analysis (IPA) a computational algorithms to identify networks
consisting of proteins of interest and their interactions with other proteins in the knowledge database.
(A)
GO term Protein
count
% EASE
score
Fold
enrichment
Benjamini
score
Protein ID
Biological process
Defence response 6 46 7.02E-05 10.99 0.010341 AHSG, HP, KNG1, A1AT, C3, TF
Inflammatory response 5 39 9.44E-05 17.34 0.009274 AHSG, KNG1, A1AT, C3, TF
Response to wounding 5 39 6.17E-04 10.63 0.025758 AHSG, KNG1, A1AT, C3, TF
Acute inflammatory response 4 31 5.83E-05 46.01 0.017115 AHSG, A1AT, C3, TF
Cellular component
Extracellular region 12 92 1.42E-09 6.36 5.54E-08 ALB, A1BG, AHSG, VTDB, HP, HPX, IGHG3, IGHM,
KNG1, A1AT, C3, I
Extracellular space 9 69 9.34E-09 13.99 1.82E-07 ALB, AHSG, VTDB, HP, HPX, KNG1, A1AT, C3, TF
Extracellular region part 9 69 1.32E-07 9.99 1.72E-06 ALB, AHSG, VTDB, HP, HPX, KNG1, A1AT, C3, TF
Molecular function
Inhibitor activity 4 31 2.11E-04 29.85 0.012572 AHSG, KNG1, A1AT, C3
Peptidase inhibitor activity 4 31 2.47E-04 28.27 0.007385 AHSG, KNG1, A1AT, C3
Enzyme inhibitor activity 4 31 0.001297 16.03 0.019277 AHSG, KNG1, A1AT, C3
Antigen binding 3 23 9.80E-04 57.96 0.019424 IGHA1, IGHG3, IGHM, IGKC
(B)
Signalling pathway Protein
count
% EASE
score
Fold
enrichment
Benjamini
score
Protein ID
Complement and coagulation
cascades
3 23 1.81E-04 73.69565 3.63E-04 KNG1, A1AT, C3
(C)
Top network Score Focus protein Protein ID
Inflammatory response,
cellular growth and
proliferation, cellular
function and
maintenance
18 9 ABCA1, ABCB1, AGER, AGT, AHSG, ALB, ANGPTL3, APOA1, APOA2, APOH,
ARF6, BMX, C3, CANX, CCL4, CEBPB, CFTR, Cg, CLDN2, CPB2, CSH1/CSH2,
DEFB103A/DEFB103B, EGFR, ELF3, FGFR2, Fibrinogen, FUT8, G6PC, GFER,
HABP2, hemoglobin, HFE, HNF1A, HNF1B, HP, HPX, Iga, IGHM, IL1, ITCH,
ITGAM, JAG1,KNG1, KRT17, LCN2,MBL2, miR-21-5p (and other miRNAs w/seed
AGCUUAU), NFkB (complex), NR1H3, NR1H4, NR5A2, PIAS3, PLC gamma,
PLD2, PPAP2B, PTPRS, RHOB, RNF31, SAA, SDC4, SERPINA1, TAB2, TAP1, TF,
TFRC, TLR5, TLR, TLR9, TNFSF13B, WWP2
Cell death and survival,
cellular assembly and
organization, connective
tissue disorders
3 1 FCGR2C, IGHG3
Cancer, gastrointestinal
disease, hepatic system
disease
3 1 ANXA2, GC
Kerishnan et al. (2016), PeerJ, DOI 10.7717/peerj.2327 13/19
interactions of proteins and chemicals. It integrates widely dispersed information
found across numerous databases and the literature (e.g., interaction with metabolic
pathways, results from binding experiments and drug-target relationships) to detect
known and predicted interactions between proteins and chemicals through genomic
context-based inferences and text-mining protocols (Kuhn et al., 2008). Based on
this analysis, we identified interactions between the indentified proteins and factors
associated with the complement and coagulation pathways (i.e., CF1, CFH, CR2,
KLKB1 and FN1). These results further support data obtained from the DAVID and
IPA analyses.
CONCLUSIONIn the present study, we found that HP, KNG1, A1AT and IGKC were differentially
expressed in sera collected from periodontal patients. Notably, the identification of
these immunogenic proteins in patient sera could contribute to early detection of
periodontitis with high specificity and sensitivity. Our findings also revealed that the
Figure 4 Interaction networks of identified host specific proteins using STITCH v4.0. The interac-
tions are displayed in confidence view. Stronger associations are represented by thicker lines. Protein-
proteins interactions are shown in blue, chemical-protein interactions in green and interactions between
chemicals are red. Chemical-chemical links are used to extend the network. The findings reveal most of
the identified proteins have an established confident link with each other in the interaction network.
Kerishnan et al. (2016), PeerJ, DOI 10.7717/peerj.2327 14/19
antibody extracted from patients with severe CP has the most immunogenic properties
to classify the distinct stages of periodontitis. Therefore, our results suggest that these
differentially expressed host immune response proteins might represent valuable
biomarkers for periodontal screening, thereby improving the accuracy of current
diagnostic methods to promote early delivery of therapeutic measures against
periodontitis. Furthermore, the role of these proteins in host inflammatory and
defence responses also highlights their importance as potential biomarkers for
periodontal disease. Further study within clinically representative populations will be
needed to validate these promising findings.
ACKNOWLEDGEMENTSThe authors would like to acknowledge the Malaysian Periodontal Database and
Biobank System (MPDBS), University of Malaya, Kuala Lumpur, Malaysia for providing
the serum samples of the periodontal diseases.
ADDITIONAL INFORMATION AND DECLARATIONS
FundingThis study was supported by the University of Malaya (UM)-High Impact Research
(HIR)-the Ministry of Education (MoE) Grant UM/C/625/1/HIR/MOE/DENT/9, UM.C/
625/1/HIR/MOHE/MED/16/5 and UM/C/625/1/HIR/MOE/DENT/4. The funders had
no role in study design, data collection and analysis, decision to publish, or preparation
of the manuscript.
Grant DisclosuresThe following grant information was disclosed by the authors:
University of Malaya (UM)-High Impact Research (HIR)-the Ministry of Education
(MoE): UM/C/625/1/HIR/MOE/DENT/9, UM.C/625/1/HIR/MOHE/MED/16/5 and
UM/C/625/1/HIR/MOE/DENT/4.
Competing InterestsThe authors declared that they have no competing interests. Yu Nieng Chen is an
employee of Chen Dental Specialist Clinic, Kuching, Sarawak, Malaysia.
Author Contributions� Jesinda P. Kerishnan performed the experiments, analyzed the data, wrote the paper,
prepared figures and/or tables.
� Sani Mohammad performed the experiments, wrote the paper.
� Muhamad Shaifunizam Alias performed the experiments, wrote the paper.
� Alan Kang-Wai Mu conceived and designed the experiments, performed the
experiments, analyzed the data, wrote the paper, prepared figures and/or tables.
� Rathna Devi Vaithilingam contributed reagents/materials/analysis tools, reviewed drafts
of the paper.
Kerishnan et al. (2016), PeerJ, DOI 10.7717/peerj.2327 15/19
� Nor Adinar Baharuddin contributed reagents/materials/analysis tools, reviewed drafts
of the paper.
� Syarida H. Safii contributed reagents/materials/analysis tools, reviewed drafts of the
paper.
� Zainal Ariff Abdul Rahman contributed reagents/materials/analysis tools, reviewed
drafts of the paper.
� Yu Nieng Chen analyzed the data, reviewed drafts of the paper.
� Yeng Chen conceived and designed the experiments, analyzed the data, contributed
reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables,
reviewed drafts of the paper.
Human EthicsThe following information was supplied relating to ethical approvals (i.e., approving body
and any reference numbers):
Serum samples were obtained from the Malaysian Periodontal Database and
Biobank System (MPDBS), University of Malaya, Kuala Lumpur, Malaysia with
approval from the Ethics Committee of the Faculty of Dentistry, University of Malaya
(DF PE1103/0037(L)).
Data DepositionThe following information was supplied regarding data availability:
MASCOT search engine and SWISS-PROT database.
Accession numbers P02765, P02768, P01009, P04217, P01024, P00738, P02790,
P01876, P01860, P01834, P01871, P01042, P02787, P02774.
REFERENCESAlbandar JM, Brunelle JM, Kingman A. 1999. Destructive periodontal disease in adults 30 years
of age and older in the United States, 1988–1994. Journal of Periodontology 70(1):13–29
DOI 10.1902/jop.1999.70.1.13.
Armitage GC. 1999. Development of a classification system for periodontal diseases and
conditions. Annals of Periodontology 4(1):1–6 DOI 10.1902/annals.1999.4.1.1.
Avrameas S. 1991. Natural autoantibodies: from ‘horror autotoxicus’ to ‘gnothi seauton’.
Immunology Today 12(5):154–159 DOI 10.1016/S0167-5699(05)80045-3.
Bartold PM, Van Dyke TE. 2013. Periodontitis: a host-mediated disruption of microbial
homeostasis. Unlearning learned concepts. Periodontology 2000 62(1):203–217
DOI 10.1111/j.1600-0757.2012.00450.x.
Boes M. 2000. Role of natural and immune IgM antibodies in immune responses. Molecular
Immunology 37(18):1141–1149 DOI 10.1016/S0161-5890(01)00025-6.
Boes M, Prodeus AP, Schmidt T, Carroll MC, Chen J. 1998. A critical role of natural
immunoglobulin M in immediate defense against systemic bacterial infection. Journal of
Experimental Medicine 188(12):2381–2386 DOI 10.1084/jem.188.12.2381.
Carlsson J, Herrmann BF, Hofling JF, Sundqvist GK. 1984. Degradation of the human proteinase
inhibitors alpha-1-antitrypsin and alpha-2-macroglobulin by Bacteroides gingivalis. Infection
and Immunity 43(2):644–648.
Kerishnan et al. (2016), PeerJ, DOI 10.7717/peerj.2327 16/19
Carrell RW. 1986. Alpha 1-Antitrypsin: molecular pathology, leukocytes, and tissue damage.
Journal of Clinical Investigation 78(6):1427–1431 DOI 10.1172/JCI112731.
Casali P, Schettino EW. 1996. Structure and function of natural antibodies. Immunology of
Silicones. Berlin, Heidelberg: Springer, 167–179.
Cekici A, Kantarci A, Hasturk H, Van Dyke TE. 2014. Inflammatory and immune pathways
in the pathogenesis of periodontal disease. Periodontology 2000 64(1):57–80
DOI 10.1111/prd.12002.
Chapple ILC. 1997. Periodontal disease diagnosis: current status and future developments.
Journal of Dentistry 25(1):3–15 DOI 10.1016/S0300-5712(95)00118-2.
Chen Y, Azman SN, Kerishnan JP, Zain RB, Chen YN, Wong YL, Gopinath SCB. 2014.
Identification of host-immune response protein candidates in the sera of human oral squamous
cell carcinoma patients. PLoS ONE 9(10):e109012 DOI 10.1371/journal.pone.0109012.
Chen Y, Lim B-K, Hashim OH. 2009. Different altered stage correlative expression of high
abundance acute-phase proteins in sera of patients with epithelial ovarian carcinoma. Journal of
Hematology & Oncology 2(1):37 DOI 10.1186/1756-8722-2-37.
Chen Y, Lim B-K, Peh S-C, Abdul-Rahman PS, Hashim OH. 2008. Profiling of serum and
tissue high abundance acute-phase proteins of patients with epithelial and germ line
ovarian carcinoma. Proteome Science 6(1):20 DOI 10.1186/1477-5956-6-20.
Coutinho A, Kazatchkine MD, Avrameas S. 1995. Natural autoantibodies. Current Opinion in
Immunology 7(6):812–818 DOI 10.1016/0952-7915(95)80053-0.
Dobryszycka W. 1997. Biological functions of haptoglobin-new pieces to an old puzzle. European
Journal of Clinical Chemistry and Clinical Biochemistry 35(9):647–654.
Eaton JW, Brandt P, Lee J. 1982. Haptoglobin: a natural bacteriostat. Science 215(4533):691–693
DOI 10.1126/science.7036344.
Ebersole JL, Machen RL, Steffen MJ, Willmann DE. 1997. Systemic acute-phase reactants,
C-reactive protein and haptoglobin, in adult periodontitis. Clinical & Experimental Immunology
107(2):347–352 DOI 10.1111/j.1365-2249.1997.270-ce1162.x.
Eke PI, Dye BA, Wei L, Thornton-Evans GO, Genco RJ. 2012. Prevalence of periodontitis in
adults in the United States: 2009 and 2010. Journal of Dental Research 91(10):914–920
DOI 10.1177/0022034512457373.
Fearon DT, Locksley RM. 1996. The instructive role of innate immunity in the acquired immune
response. Science 272(5258):50–54 DOI 10.1126/science.272.5258.50.
Gottlieb PD, Cunningham BA, Rutishauser US, Edelman GM. 1970. Covalent structure of a
human �G-immunoglobulin. VI. Amino acid sequence of the light chain. Biochemistry
9(16):3155–3161 DOI 10.1021/bi00818a007.
Guevara M, Terra C, Nazar A, Sola E, Fernandez J, Pavesi M, Arroyo V, Gines P. 2012. Albumin
for bacterial infections other than spontaneous bacterial peritonitis in cirrhosis. A randomized,
controlled study. Journal of Hepatology 57(4):759–765 DOI 10.1016/j.jhep.2012.06.013.
Hatem AE. 2012. Epidemiology and Risk Factors of Periodontal Disease. Rijeka: INTECH Open
Access Publisher.
Heukeshoven J, Dernick R. 1988. Improved silver staining procedure for fast staining in
PhastSystem development unit. I. Staining of sodium dodecyl sulfate gels. Electrophoresis
9(1):28–32 DOI 10.1002/elps.1150090106.
Huang DW, Sherman BT, Lempicki RA. 2008. Systematic and integrative analysis of large gene
lists using DAVID bioinformatics resources. Nature Protocols 4(1):44–57
DOI 10.1038/nprot.2008.211.
Kerishnan et al. (2016), PeerJ, DOI 10.7717/peerj.2327 17/19
Iarovaia G. 2000. Kallikrein-kinin system: novel facts and concepts (literature review).
Voprosy Meditsinskoi Khimii 47(1):20–42.
Imamura T, Potempa J, Travis J. 2004. Activation of the kallikrein-kinin system and release of
new kinins through alternative cleavage of kininogens by microbial and human cell proteinases.
Biological Chemistry 385(11):989–996 DOI 10.1515/BC.2004.129.
Jefferis R, Lefranc M-P. 2009. Human immunoglobulin allotypes: possible implications for
immunogenicity. MAbs 1(4):332–338.
Kapralov A, Vlasova II, Feng W, Maeda A, Walson K, Tyurin VA, Huang Z, Aneja RK, Carcillo J,
Bayır H, Kagan VE. 2009. Peroxidase activity of hemoglobin haptoglobin complexes: covalent
aggregation and oxidative stress in plasma and macrophages. Journal of Biological Chemistry
284(44):30395–30407 DOI 10.1074/jbc.M109.045567.
Kinney JS, Ramseier CA, Giannobile WV. 2007. Oral fluid–based biomarkers of alveolar bone
loss in periodontitis. Annals of the New York Academy of Sciences 1098(1):230–251
DOI 10.1196/annals.1384.028.
Kuhn M, von Mering C, Campillos M, Jensen LJ, Bork P. 2008. STITCH: interaction networks
of chemicals and proteins. Nucleic Acids Research 36(Supp 1):D684–D688
DOI 10.1093/nar/gkm795.
Lin H-S, Talwar HS, Tarca AL, Ionan A, Chatterjee M, Ye B, Wojciechowski J, Mohapatra S,
Basson MD, Yoo GH, Peshek B, Lonardo F, Pan C-JG, Folbe AJ, Draghici S, Abrams J,
Tainsky MA. 2007. Autoantibody approach for serum-based detection of head and neck cancer.
Cancer Epidemiology Biomarkers & Prevention 16(11):2396–2405
DOI 10.1158/1055-9965.EPI-07-0318.
Loe H, Theilade E, Jensen SB. 1965. Experimental gingivitis in man. Journal of Periodontology
36(3):177–187 DOI 10.1902/jop.1965.36.3.177.
Markiewski MM, Nilsson B, Ekdahl KN, Mollnes TE, Lambris JD. 2007. Complement and
coagulation: strangers or partners in crime? Trends in Immunology 28(4):184–192
DOI 10.1016/j.it.2007.02.006.
Matsuzaki G, Vordermeier HM, Hashimoto A, Nomoto K, Ivanyi J. 1999. The role of
B cells in the establishment of T cell response in mice infected with an intracellular
bacteria, Listeria monocytogenes. Cellular Immunology 194(2):178–185
DOI 10.1006/cimm.1999.1503.
Pereira P, Forni L, Larsson EL, Cooper M, Heusser C, Coutinho A. 1986. Autonomous activation
of B and T cells in antigen-free mice. European Journal of Immunology 16(6):685–688
DOI 10.1002/eji.1830160616.
Preshaw PM. 2009. Definitions of periodontal disease in research. Journal of Clinical
Periodontology 36(1):1–2 DOI 10.1111/j.1600-051X.2008.01320.x.
Ramfjord SP. 1959. Indices for prevalence and incidence of periodontal disease. Journal of
Periodontology 30(1):51–59 DOI 10.1902/jop.1959.30.1.51.
Saunders DN, Tindall EA, Shearer RF, Roberson J, Decker A, Wilson JA, Hayes VM. 2012.
A novel SERPINA1 mutation causing serum alpha1-antitrypsin deficiency. PLoS ONE
7(12):e51762 DOI 10.1371/journal.pone.0051762.
Skaar EP. 2010. The battle for iron between bacterial pathogens and their vertebrate hosts.
PLoS Pathogens 6(8):e1000949 DOI 10.1371/journal.ppat.1000949.
Snevechenko A, Wilm M, Vorm O, Mann M. 1996. Mass spectrometric sequencing of proteins
silver-stained polyacrilamide gels. Analytical Chemistry 68(5):850–858.
Kerishnan et al. (2016), PeerJ, DOI 10.7717/peerj.2327 18/19
Starkey PM, Barrett A. 1977. a2-Macroglobulin, a physiological regulator of proteinase activity.
In: Barrett AJ, ed. Proteinases in Mammalian Cells and Tissues. Amsterdam: Elsevier/North
Holland Biomedical Press, 663–696.
Susin C, Haas AN, Albandar JM. 2014. Epidemiology and demographics of aggressive
periodontitis. Periodontology 2000 65(1):27–45 DOI 10.1111/prd.12019.
Tlaskalova-Hogenova H, Mandel L, Stepankova R, Bartova J, Barot R, Leclerc M, Kovaru F,
Trebichavsky I. 1991. Autoimmunity: from physiology to pathology. Natural antibodies,
mucosal immunity and development of B cell repertoire. Folia Biologica 38(3–4):202–215.
Vaithilingam R, Safii S, Baharuddin N, Karen-Ng LP, Saub R, Ariffin F, Ramli H, Sharifuddin A,
Hidayat MFH, Raman R, Chan YK, Rani NA, Rahim RA, Shahruddin N, Cheong SC,
Bartold PM, Zain RB. 2015. Establishing and managing a periodontal biobank for research:
the sharing of experience. Oral Diseases 21(1):e62–e69 DOI 10.1111/odi.12267.
Van Der Velden U. 2005. Purpose and problems of periodontal disease classification.
Periodontology 2000 39(1):13–21 DOI 10.1111/j.1600-0757.2005.00127.x.
Vollmers HP, Brandlein S. 2006. Natural IgM antibodies: the orphaned molecules in
immune surveillance. Advanced Drug Delivery Reviews 58(5–6):755–765
DOI 10.1016/j.addr.2005.08.007.
Wassell J. 1999. Haptoglobin: function and polymorphism. Clinical Laboratory
46(11–12):547–552.
Wiebe CB, Putnins EE. 2000. The periodontal disease classification system of the
American Academy of Periodontology–an update. Journal of Canadian Dental Association
66(11):594–597.
Kerishnan et al. (2016), PeerJ, DOI 10.7717/peerj.2327 19/19